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The purpose of this paper is to report on the implementation of the World Health Organization (WHO) trauma care checklist (TCC) (WHO, 2016) in an emergency department in a tertiary hospital in Nepal. This research was undertaken as part of a Hybrid International Emergency Medicine Fellowship programme (Subedi et al., 2020) across UK and Nepal, incorporating a two-year rotation through the UK National Health Service, via the Medical Training Initiative (MTI) (AoMRC, 2017). The WHO TCC can improve outcomes for trauma patients (Lashoher et al., 2016); however, significant barriers affect its implementation worldwide (Nolan et al., 2014; Wild et al., 2020). This article reports on the implementation, barriers and recommendations of WHO TCC implementation in the context of Nepal and argues for Transformational Leadership (TL) to support its implementation.

Explanatory mixed methods research (Creswell, 2014), comprising quasi-experimental research and a qualitative online survey, were selected methods for this research. A training module was designed and implemented for 10 doctors and 15 nurses from a total of 76 (33%) of clinicians to aid in the introduction of the WHO TCC in an emergency department in a hospital in Nepal. The quasi-experimental research involved a pre- and post-training survey aimed to assess participant’s knowledge of the WHO TCC before and after training and before the implementation of the WHO TCC in the emergency department. Post-training, 219 patients were reviewed after four weeks to identify if process measures had improved the quality of care to trauma patients. Subsequently six months later, a qualitative online survey was sent to all clinical staff in the department to identify barriers to implementation, with a response rate of 26 (n = 26) (34%) (20 doctors and 6 nurses). Descriptive statistics were used to evaluate quantitative data and the qualitative data were analysed using the five stepped approach of thematic analysis (Braun and Clarke, 2006).

The evaluation of the implementation of the WHO TCC showed an improvement in care for trauma patients in an emergency setting in a tertiary hospital in Nepal. There were improvements in the documentation in trauma management, showing the training had a direct impact on the quality of care of trauma patients. Notably, there was an improvement in cervical spine examination from 56.1% before training to 78.1%; chest examination 125 (57.07%) before training and 170 (77.62%) post-training; abdominal examination 121 (55.25%) before training and 169 (77.16%) post-training; gross motor examination 13 (5.93%) before training and 131 (59.82%) post-training; sensory examination 4 (1.82%) before training and 115 (52.51%) post-training; distal pulse examination 6 (2.73%) before training and 122 (55.7%) post-training. However, while the quality of documentation for trauma patients improved from the baseline of 56%, it only reached 78% when the percentage improvement target agreed for this research project was 90%. The 10 (n = 10) doctors and 15 (n = 15) nurses in the Emergency Department (ED) all improved their baseline knowledge from 72.2% to 87% (p = 0.00006), by 14.8% and 67% to 85%) (p = 0.006), respectively. Nurses started with lower scores (mean 67) in the baseline when compared to doctors, but they made significant gains in their learning post-training. The qualitative data reported barriers, such as the busyness of the department, with residents and medical officers, suggesting a shortened version of the checklist to support greater protocol compliance. Embedding this research within TL provided a steer for successful innovation and change, identifying action for sustaining change over time.

The study is a single-centre study that involved trauma patients in an emergency department in one hospital in Nepal. There is a lack of internationally recognised trauma training in Nepal and very few specialist trauma centres; hence, it was challenging to teach trauma to clinicians in a single 1-h session. High levels of transformation of health services are required in Nepal, but the sample for this research was small to test out and pilot the protocol to gain wider stakeholder buy in. The rapid turnover of doctors and nurses in the emergency department, creates an additional challenge but encouraging a multi-disciplinary approach through TL creates a greater chance of sustainability of the WHO TCC.

International protocols are required in Nepal to support the transformation of health care. This explanatory mixed methods research, which is part of an International Fellowship programme, provides evidence of direct improvements in the quality of patient care and demonstrates how TL can drive improvement in a low- to medium-income country.

The Nepal/UK Hybrid International Emergency Medicine Fellowships have an opportunity to implement changes to the health system in Nepal through research, by bringing international level standards and protocols to the hospital to improve the quality of care provided to patients.

To the best of the authors’ knowledge, this research paper is one of the first studies of its kind to demonstrate direct patient level improvements as an outcome of the two-year MTI scheme.

This study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.

In this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.

This study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.

According to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.